A FastAPI client and a MCPServer client for Qdrant access as a service

The file main.py is the entry point and a command line argument selects which server you want to run.

main.py

import argparse
import uvicorn
from fastapi_server import app as fastapi_app
from fastmcp_server import app as fastmcp_app
from config import settings

def run_fastapi():
    """Run the FastAPI server"""
    print(f"Starting FastAPI server on port {settings.port}")
    uvicorn.run(
        fastapi_app,
        host="0.0.0.0",
        port=settings.port,
        log_level="info"
    )

def run_fastmcp():
    """Run the FastMCP server"""
    print(f"Starting FastMCP server on port {settings.mcp_port}")
    fastmcp_app.run(port=settings.mcp_port)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Run Qdrant MCP Server")
    parser.add_argument(
        "--server-type",
        choices=["fastapi", "fastmcp"],
        default="fastmcp",
        help="Type of server to run (default: fastmcp)"
    )
    args = parser.parse_args()

    if args.server_type == "fastapi":
        run_fastapi()
    else:
        run_fastmcp()

Qdrant MCP Server

A dual-protocol server for Qdrant knowledge graph operations, supporting both FastAPI and FastMCP protocols.

Project Structure

src/qdrant_mcpserver/
├── __init__.py
├── config.py          # Configuration settings
├── qdrant_client.py   # Qdrant operations
├── fastapi_server.py  # FastAPI implementation
├── fastmcp_server.py  # FastMCP implementation
└── main.py            # CLI entry point

File Descriptions

config.py

• Loads environment variables
• Contains settings for:
  • Qdrant connection (URL, API key)
  • OpenAI API key
  • Collection names
  • Server ports
• Uses pydantic for validation

qdrant_client.py

• Implements core Qdrant operations:
  • Collection management
  • Node upsert/delete
  • Vector search
• Handles embedding generation via OpenAI
• Provides service layer for both server types

fastapi_server.py

• FastAPI implementation with:
  • RESTful endpoints
  • CORS middleware
  • OpenAPI documentation
• Endpoints:
  • POST /nodes/upsert
  • POST /nodes/search
  • DELETE /nodes
  • GET /health

fastmcp_server.py

• FastMCP implementation with:
  • MCP protocol compliance
  • Authentication support
  • Standardized response formats
• Same endpoints as FastAPI but with MCP envelope

main.py

• CLI entry point with:
  • Server type selection (--server-type)
  • Unified logging
  • Port configuration
• Runs either FastAPI or FastMCP server

Installation

1. Install Poetry (if not installed):

curl -sSL https://install.python-poetry.org | python3 -

2. Clone repository:

git clone https://github.com/your-repo/qdrant-mcpserver.git
cd qdrant-mcpserver

3. Install dependencies:

poetry install

4. Configure environment:

cp .env.example .env
# Edit .env with your actual values

Usage

Running the Server

# Run FastMCP server (default)
poetry run python -m qdrant_mcpserver.main

# Run FastAPI server
poetry run python -m qdrant_mcpserver.main --server-type fastapi

Environment Variables

Variable	Required	Description
QDRANT_URL	Yes	Qdrant server URL
QDRANT_API_KEY	No	Qdrant API key
OPENAI_API_KEY	Yes	OpenAI API key
COLLECTION_NAME	No	Default: "knowledge_graph"
PORT	No	FastAPI port (default: 8000)
MCP_PORT	No	FastMCP port (default: 8080)
MCP_SECRET	No	Authentication secret

API Endpoints

Both servers provide the same endpoints:

• POST /nodes/upsert - Upsert knowledge graph nodes
• POST /nodes/search - Semantic search across nodes
• DELETE /nodes - Delete nodes by IDs
• GET /health - Health check

Development

Code Formatting

These commands ensure consistent code style:

# Formats code according to Black's style guide (PEP 8 compliant)
poetry run black .

Organizes imports properly (groups standard lib, third-party, local)

poetry run isort .Format code:

poetry run black .
poetry run isort .

Testing

Using pytest for comprehensive test coverage. Test files should mirror the main code structure:

Setup tests

poetry install --with test
poetry run pytest --cov --cov-report=html

# Run all tests
poetry run pytest -v

# Run with coverage report
poetry run pytest --cov=qdrant_mcpserver --cov-report=term-missing
Setup tests (one time):

Type checking:

poetry run mypy .

Deployment

Build production package:

poetry build

Install system-wide:

pip install dist/*.whl

Run as service:

python -m qdrant_mcpserver.main --server-type fastmcp

Key Features:

1. Flexible Server Selection:

   • CLI argument chooses between FastAPI and FastMCP
   • Shared configuration and Qdrant client
   • Consistent endpoints across both

2. Comprehensive Documentation:

   • Clear file structure explanation
   • Installation and usage instructions
   • Environment variable reference
   • Development workflow

3. Production-Ready:

   • Poetry for dependency management
   • Configuration via environment variables
   • Build and deployment instructions

4. Maintainable Structure:

   • Separation of concerns
   • Shared core functionality
   • Clear development practices

The implementation allows you to switch between server protocols while maintaining the same underlying Qdrant operations.